Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs
Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs
复制标题
模糊回归不连续性设计中的偏差感知推理
DOI：
10.3982/ecta19466
复制
发表时间：
2019
期刊：
影响因子：
6.1
通讯作者：
C. Rothe
中科院分区：
文献类型：
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作者：
C. Noack;C. Rothe
针对模糊设计中的回归间断参数，提出了一种新的置信度。我们的CS是基于局部线性回归的，并且是有偏差意识的，因为它们明确地考虑了可能的偏差。它们的结构与Anderson-Rubin CS在精确识别工具变量模型方面的相似之处，从而避免了作为模糊回归不连续性分析最常用现有推理方法的基础的“Delta方法”近似的问题。我们的CS与规范环境中的现有过程渐近等价，具有强识别性和连续运行变量。然而，它们在其他广泛的经验相关条件下也是有效的，例如具有离散运行变量的设置、甜甜圈设计和弱识别。
We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias‐aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson–Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with “delta method” approximations that underlie most commonly used existing inference methods for fuzzy regression discontinuity analysis. Our CSs are asymptotically equivalent to existing procedures in canonical settings with strong identification and a continuous running variable. However, they are also valid under a wide range of other empirically relevant conditions, such as setups with discrete running variables, donut designs, and weak identification.
DOI：
10.1146/annurev-economics-080218-025643
发表时间：
2019-01-01
期刊：
ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019
影响因子：
--
作者：
Andrews, Isaiah;Stock, James H.;Sun, Liyang
通讯作者：
Sun, Liyang